AI video technology is still nowhere even remotely near just "click a button and take what it spits out". I don't know how to break this to anyone here, but you're not just going to go to some video generation site and turn out Woodnuts without extensive skill about AI video tools themselves and a wide range of traditional video production tools, and without spending weeks to months and significant financial expense on the project.

Even if / when this changes, video production is still always going to be limited by the human at hand. Most people's movie ideas, plotting, scripting, directing, etc frankly will be terrible. The slop in this case is the human, not the tool.

Well, I always thought Algol68 should have had more penetration. People said it was too complicated, but they hadn't seen modern C++.

On the upside, AI lets anyone make a movie.
On the downside, AI lets anyone make a movie.

Including people who have terrible taste in plot, style, and everything else.

There's some genuinely good stuff out there - Gossip Goblin's work for example. But this is....

I'll just say, there's far better things that one could have spent half a million dollars on...

It's not that exotic. Lightning can jam radio networks, and some places it's not uncommon.

You're assuming it didn't quickly dry out. I've made bread that could sit around that long. But you wouldn't want to eat it without soaking it first (probably in soup).

Wireless has failure modes that wired communications don't. They probably can't avoid some of the failure modes, like jamming. And there are places where wireless just doesn't reach...which aren't the same as the places where wired can't reach. I used to live in a ... large gully or small canyon. Wired had no problem, but it was essentially radio dead.

How are you defining "statistical inferences" as distinct from "logical inferences"? If you're defining fuzzy logic (e.g. not necessarily yes-or-no answers but allowing for ambiguity in conclusions), then we can agree conceptually, but your choice of wording is, I have to say, weird if so.

The question is more "Will we be leading in anything by 2035?".

That we stopped leading by the end of the 21st century would just be normal. Leading countries don't remain leading forever. I'm not sure Britain managed to be the leading country for 150 years. In the 1800's it was contesting with France for the title, and by around 1950 the US was the acknowledged leader. So 1950-2100 would be about the same span of time.

My understanding is that LLMs are built on a foundation of ANNs, and that indeed the backpropagation used to train ANNs is a statistical process;

Two responses. One, that's discussing individual-neuron scale processes rather than collective processes; and this was a discussion about inference, not training. Human neurons also learn by error minimization (Hebbian learning). But this does not describe the macroscopic processes that result from said minimization.

* During training, neurons develop into classifiers that detect superpositions of concepts that collectively follow the same activation process. Individual neurons weight their input space and subdivide it by a fuzzy hyperplane to achieve a classification result.

* In subsequent layers, said input space is formed from a weighted combination of the previous layer's classification; thus, the superpositions of questions being formed are more complex, as are the classification results.

* In a LLM, this iterates for dozens of layers, gaining complexity at each layer, to form each FFN

* The initial input space to a FFN is a latent (conceptual representation), as is the output; the FFNs, in result, function as classifier-generators; they detect combinations of concepts in the input space, and output the causally-resultant concepts into the output space

* FFNs alternate with attention layers dozens to hundreds of times in order to process the information, each layer building on the results of the previous one.

The word to describe that is not "statistics". It's "logic".

In a LLM, the first few layers focus on disambiguation. If there's a token for "bank", is this about a riverbank, a financial bank, banking a plane, etc? As the layers progress, it starts building up first simple circuits, and then progressively more complex circuits - you might get a circuit that detects "talking like MAGA", or "off-by-one programming errors", or whatnot. In the late layers, you have the general conclusions reached - for example, if it were "The capitol of the state that contains America's fourth-largest metro area is...", you've already had FFNs detect the concepts of fourth-largest metro area and encoded Dallas-Forth Worth, and then later taken that and encoded "Texas", and then finally encoding "Austin". And then in the final couple layers you converge back toward linguistic space.

Anthropic has done some great work on this with attribution graph probes and the like; you can detect what circuits are firing, and on what things those circuits fire, and ramp them up or down to see how it modifies the output. They very much work through long chains of logical inferences.

The US probably is leading this week. But China has been making several recent announcements, and possibly when those get in the field (well, observable, they're claimed to already be in use) that will change.
OTOH...

Speculating about a rapidly changing field is always problematic.

I use every style imaginable, including photos, in my tests. Same result every time.

One time I even did it with a Calvin and Hobbes comic, pretending than an AI made it. Responses included things like "The illustration also looks like shit and barely makes sense. Hope that helps.", "God damn this sucks so bad", "This also fucking sucks", and "The only punchline here is casual, pointless cruelty. if you think this is funny then you're literally a psychopath."

LLMs are not "statistical models" (randomness only even comes into play in the final conversion from latent space to token space because latent space is high dimensional, token space is low dimension, you need a rounding mechanism, and a "noisy" rounding mechanism works best; what you're thinking of, by contrast, is Markov models). And you cannot just "get lucky and randomly solve an unsolved math problem"; that's not how any of this works.

Also, it's silly that people are acting like "all problems but this one were already in the literature". AI has solved a whole slew on Erdos problems, and only a fraction had anything to do with existing literature.

And even in "existing literature" examples, it's not "nobody ever thought to search before" as if all mathematicians are morons, or that mathematicians adore putting out Erdos problem solutions without claiming them, It's that nobody had ever thought to apply an obscure technique from a given piece of literature to said Erdos problem.

The simple fact is, AI has gotten much better at solving unsolved math problems than humans are. It's simply another field that it's taking over, the same way it has been taking over programming. One can debate how much is "clever insight" vs. "just chugging away at possibilities until it hits on ways to advance toward the goal", but ultimately, that's a distraction from the fact that: it's getting really good at solving math problems that humans have spent decades on without success.

Clearly that wasn't spam, because his name is nospam007.

Forced? You think reviewers of writing contests are captives, imprisoned by mad scientists and forced to read story after story, like some sort of literary equivalent of the backstory to MST3K?